radar backscatter mapping using terrasar-x

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3538 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011 Radar Backscatter Mapping Using TerraSAR-X Paola Rizzoli, Benjamin Bräutigam, Steffen Wollstadt, and Josef Mittermayer Abstract—Global backscatter data can be used for accurate synthetic aperture radar (SAR) performance estimation and opti- mizing instrument settings for SAR systems, e.g. the TerraSAR-X mission (TSM) and the TanDEM-X mission (TDM). The goal of this work is the generation of X-band backscatter maps by mosaicking images acquired by the TSM. An algorithm that allows the estimation of the on-ground backscatter, for any required polarization and incidence angle from the available data, is imple- mented. In this paper, the backscatter map generation algorithm is presented, together with the first results, obtained from the TSX-1 data. The validity of the interpolation models is also discussed, as they will form the basis for a future global statistical analysis and modeling of backscatter behavior in X-band SAR data. Index Terms—Mosaic generation, radar brightness, TanDEM-X (TDX-1), TerraSAR-X (TSX-1), X-band backscatter. I. I NTRODUCTION S YNTHETIC aperture radar (SAR) systems play a key role in the field of remote sensing. They provide useful data for a large number of scientific applications, such as hydrology, glaciology, forestry, and oceanography. Large-scale mosaics allow the global monitoring of the Earth’s environmen- tal change. Up to now, global mosaics from spaceborne SAR data have been generated for C-band using European Space Agency (ESA) Envisat data [5] and L-band using advanced land observing satellite (ALOS) phased array type L-band synthetic aperture radar (PALSAR) data [6]. No global backscatter map for X-band has been provided yet, even though spaceborne X-band SAR systems have established themselves as a ma- jor source for current and future remote sensing applications [1]–[4], [7]. TerraSAR-X (TSX-1) and its twin satellite TanDEM-X (TDX-1) are two German SAR satellites developed in a pub- lic/private partnership between the German Aerospace Center (DLR) and EADS Astrium. Two different missions are actually ongoing, exploiting data acquired by both satellites. The first one is the TSX-1 mission (TSM), started in June 2007, which combines the ability to acquire high-resolution images with the Manuscript received September 29, 2010; revised February 18, 2011 and May 20, 2011; accepted June 7, 2011. Date of publication September 7, 2011; date of current version September 28, 2011. P. Rizzoli and B. Bräutigam are with the Satellite SAR Systems Department, Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, Germany (e-mail: [email protected]; Benjamin.Braeutigam@ dlr.de). S. Wollstadt and J. Mittermayer are with the Radar Concepts Department, Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, Germany (e-mail: [email protected]; josef.mittermayer@ dlr.de). Digital Object Identifier 10.1109/TGRS.2011.2161874 acquisition of wide-swath images for large-scale applications. In this case, TSX-1 and TDX-1 separately acquire independent ground scenes. The second one is the TDX-1 mission (TDM), started in June 2010, whose primary target is the generation of a worldwide high-precision digital elevation model (DEM) [1]. Since October 2010, both satellites fly in close-orbit configu- ration, enabling the acquisition of highly accurate cross- and along-track interferograms. High accuracy in commanding, processing, and system per- formance is required in order to provide such information [8], [9]. Precise X-band backscatter knowledge is necessary for an optimized operation of the whole SAR system inside the TSM and TDM, where no automatic adaptation of commanding radar parameters is performed on-board during a data take. Acquisitions are commanded with predefined receiver gain setting, and no automatic gain control is performed. For known backscatter characteristics of a requested SAR scene, such gain can be suitably adapted to mitigate clipping or signal saturation [9]. For realistic performance prediction of SAR images and DEM products, the backscatter information is a valuable input, e.g., for signal-to-noise ratio (SNR) estimation and height error calculation. Moreover, the availability of X-band backscatter maps, generated from TSX-1 data, leads to many further scien- tific applications, such as the monitoring of X-band backscatter evolution in time and the study of radar reflectivity behavior depending on land type and soil conditions. Radar backscatter is defined as the portion of a radar signal that the target redirects back toward the radar antenna. Its properties change depending on several factors, such as sensor parameters (e.g., frequency and polarization), soil conditions, surface roughness, and topographic characteristics of the illu- minated ground area, particularly the local incidence angle of illumination. Different quantities can be used to represent the backscatter from SAR data, as described in [10] and [11]. The radar brightness β 0 is the only one that does not require knowl- edge of the local incidence angle. In order to avoid mistakes due to limited knowledge of ground topography (which impacts on the estimation of the local incidence angle), β 0 will be used in this paper as radar backscatter mapping quantity. Several models have been provided in the past for the char- acterization of backscatter behavior, depending on different parameters, such as frequency, polarization, incidence angle, and on-ground vegetation. Well known and widely used is the database provided by Ulaby and Dobson in 1989 [10]. Some of the derived models have been used for this work, as will be explained in Section II. The large amount of high-quality TSX-1 data allows the statistical analysis of X-band backscatter from space, for different test sites and acquisition dates. Up to December 2010, more than 40 000 images have already been acquired. The long-term objective of the work presented in this 0196-2892/$26.00 © 2011 IEEE

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Page 1: Radar Backscatter Mapping Using TerraSAR-X

3538 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011

Radar Backscatter Mapping Using TerraSAR-XPaola Rizzoli, Benjamin Bräutigam, Steffen Wollstadt, and Josef Mittermayer

Abstract—Global backscatter data can be used for accuratesynthetic aperture radar (SAR) performance estimation and opti-mizing instrument settings for SAR systems, e.g. the TerraSAR-Xmission (TSM) and the TanDEM-X mission (TDM). The goalof this work is the generation of X-band backscatter maps bymosaicking images acquired by the TSM. An algorithm that allowsthe estimation of the on-ground backscatter, for any requiredpolarization and incidence angle from the available data, is imple-mented. In this paper, the backscatter map generation algorithm ispresented, together with the first results, obtained from the TSX-1data. The validity of the interpolation models is also discussed, asthey will form the basis for a future global statistical analysis andmodeling of backscatter behavior in X-band SAR data.

Index Terms—Mosaic generation, radar brightness, TanDEM-X(TDX-1), TerraSAR-X (TSX-1), X-band backscatter.

I. INTRODUCTION

SYNTHETIC aperture radar (SAR) systems play a keyrole in the field of remote sensing. They provide useful

data for a large number of scientific applications, such ashydrology, glaciology, forestry, and oceanography. Large-scalemosaics allow the global monitoring of the Earth’s environmen-tal change. Up to now, global mosaics from spaceborne SARdata have been generated for C-band using European SpaceAgency (ESA) Envisat data [5] and L-band using advanced landobserving satellite (ALOS) phased array type L-band syntheticaperture radar (PALSAR) data [6]. No global backscatter mapfor X-band has been provided yet, even though spaceborneX-band SAR systems have established themselves as a ma-jor source for current and future remote sensing applications[1]–[4], [7].

TerraSAR-X (TSX-1) and its twin satellite TanDEM-X(TDX-1) are two German SAR satellites developed in a pub-lic/private partnership between the German Aerospace Center(DLR) and EADS Astrium. Two different missions are actuallyongoing, exploiting data acquired by both satellites. The firstone is the TSX-1 mission (TSM), started in June 2007, whichcombines the ability to acquire high-resolution images with the

Manuscript received September 29, 2010; revised February 18, 2011 andMay 20, 2011; accepted June 7, 2011. Date of publication September 7, 2011;date of current version September 28, 2011.

P. Rizzoli and B. Bräutigam are with the Satellite SAR Systems Department,Microwaves and Radar Institute, German Aerospace Center (DLR), 82234Wessling, Germany (e-mail: [email protected]; [email protected]).

S. Wollstadt and J. Mittermayer are with the Radar Concepts Department,Microwaves and Radar Institute, German Aerospace Center (DLR), 82234Wessling, Germany (e-mail: [email protected]; [email protected]).

Digital Object Identifier 10.1109/TGRS.2011.2161874

acquisition of wide-swath images for large-scale applications.In this case, TSX-1 and TDX-1 separately acquire independentground scenes. The second one is the TDX-1 mission (TDM),started in June 2010, whose primary target is the generation ofa worldwide high-precision digital elevation model (DEM) [1].Since October 2010, both satellites fly in close-orbit configu-ration, enabling the acquisition of highly accurate cross- andalong-track interferograms.

High accuracy in commanding, processing, and system per-formance is required in order to provide such information [8],[9]. Precise X-band backscatter knowledge is necessary foran optimized operation of the whole SAR system inside theTSM and TDM, where no automatic adaptation of commandingradar parameters is performed on-board during a data take.Acquisitions are commanded with predefined receiver gainsetting, and no automatic gain control is performed. For knownbackscatter characteristics of a requested SAR scene, such gaincan be suitably adapted to mitigate clipping or signal saturation[9]. For realistic performance prediction of SAR images andDEM products, the backscatter information is a valuable input,e.g., for signal-to-noise ratio (SNR) estimation and height errorcalculation. Moreover, the availability of X-band backscattermaps, generated from TSX-1 data, leads to many further scien-tific applications, such as the monitoring of X-band backscatterevolution in time and the study of radar reflectivity behaviordepending on land type and soil conditions.

Radar backscatter is defined as the portion of a radar signalthat the target redirects back toward the radar antenna. Itsproperties change depending on several factors, such as sensorparameters (e.g., frequency and polarization), soil conditions,surface roughness, and topographic characteristics of the illu-minated ground area, particularly the local incidence angle ofillumination. Different quantities can be used to represent thebackscatter from SAR data, as described in [10] and [11]. Theradar brightness β0 is the only one that does not require knowl-edge of the local incidence angle. In order to avoid mistakes dueto limited knowledge of ground topography (which impacts onthe estimation of the local incidence angle), β0 will be used inthis paper as radar backscatter mapping quantity.

Several models have been provided in the past for the char-acterization of backscatter behavior, depending on differentparameters, such as frequency, polarization, incidence angle,and on-ground vegetation. Well known and widely used is thedatabase provided by Ulaby and Dobson in 1989 [10]. Someof the derived models have been used for this work, as willbe explained in Section II. The large amount of high-qualityTSX-1 data allows the statistical analysis of X-band backscatterfrom space, for different test sites and acquisition dates. Up toDecember 2010, more than 40 000 images have already beenacquired. The long-term objective of the work presented in this

0196-2892/$26.00 © 2011 IEEE

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RIZZOLI et al.: RADAR BACKSCATTER MAPPING USING TerraSAR-X 3539

paper is to derive X-band backscatter trends from up-to-dateTSX-1 data, depending on the following parameters:

1) polarization;2) vegetation class;3) local incidence angle;4) acquisition time;5) seasonal time.Moreover, the up-to-date set of statistical data represents a

valuable input for the validation of the developed backscattermap generation algorithm (Section II-B).

This paper is structured in the following way. Section IIshows the details of the implemented algorithm for the genera-tion of backscatter maps, analyzing both an individual subsam-pling algorithm, that has to be applied to each input product,as well as the final mosaic generation procedure. Section IIIdescribes the algorithm developed to fill eventually remaininggaps inside the output map. The first resulting global and on-demand backscatter maps are presented in Section IV. Finally,Section V introduces the statistical approach that will be used toderive up-to-date backscatter models for X-band and to validatethe backscatter map generation algorithm itself.

II. BACKSCATTER MAP ALGORITHM

The algorithm developed for the generation of backscattermaps consists of two steps and allows a flexible selection ofinput and output settings. This means that, on the one hand,a default set of parameters is determined in order to generate aglobal map from available TSX-1 data. On the other hand, localor global backscatter maps can be generated as well, dependingon the user purposes, setting, e.g., a desired output map reso-lution, a specific orbit direction, and an acquisition time of theSAR image input products. The first algorithm step consists of adedicated subsampling procedure applied to each input product,retrieving a matrix of radar brightness values β0. The secondstep is the mosaic generation, consisting of the assembly of allthe available radar brightness data that suit the predefined inputparameters, such as polarization and acquisition time, and arelocated inside the required output ground region.

A. Step 1: β0 Database Generation

One of the main goals of this work is the generation of aglobal X-band backscatter map from TSX-1 data. The pro-cessing time of all input data needs to be optimized, andmoreover, since high resolution is not needed, only TSX-1quick-look images are considered [12]. Such images provideless resolution regarding the standard TSX-1 single-look slant-range complex products (SSC Level-1b) [13] and are absolutelycalibrated according to the TSX-1 absolute calibration constant.Table I shows the resolution and pixel spacing of TSX-1 basicSAR products and quick-look images, for different acquisitionmodes. The default resolution of the output global backscattermap has been set to 5× 5 km2 at the equator, corresponding toan angular spacing of 0.05◦ in latitude/longitude coordinates.Such a pixel spacing provides a good compromise between theneeded backscatter map resolution required in commanding andthe total data size of the output global backscatter map. For this

TABLE ITSX-1 AZIMUTH AND DERIVED GROUND RANGE RESOLUTIONS (FOR

SSC LEVEL-1B PRODUCTS) AND PIXEL SPACING (FOR QUICK-LOOK

IMAGES AND FINAL β0 DATABASE). GROUND RANGE RESOLUTION IS

REFERRED TO FAR AND NEAR RANGE, RESPECTIVELY. (SM) STRIPMAP.(SC) SCANSAR. (SL) SPOTLIGHT. (HS) HIGH-RESOLUTION SPOTLIGHT

reason, the input quick-look images are initially averaged byan acquisition-mode depending factor, which reduces the totalamount of data that has to be stored and provides images ofsimilar resolution (see Table I). For example, averaging factorsof two and ten are applied to ScanSAR and Spotlight products,respectively, leading to a final pixel spacing of 100 m.

Each subsampled image is next geocoded by interpolat-ing from azimuth/slant-range geometry into latitude/longitude/height coordinates following the conversion matrices alreadyprovided within the quick-look images [13].

A matrix of incidence angles associated to the calibratedproduct is evaluated as well. Due to computing costs and thelimited precision of the currently available DEMs, an approx-imation is made to calculate the local incidence angle, takinginto account the sensor position and the WGS84 ground targetcoordinates only, expressed in terms of latitude, longitude, andheight. Nevertheless, an update will be made by using thehigh-resolution DEMs that will be provided at the end of theTDM [1], taking into account the local slope of the illuminatedground area as well.

Every single processed product is now stored inside adatabase, together with additional parameters connected to it,such as the geocoded coordinate matrix, the acquisition time,the satellite orbit direction, and the polarization channel. Allthese metadata are then available for the generation of specificbackscatter maps.

B. Step 2: Mosaic Generation

The generation of the backscatter mosaic consists of thecombination of the individual β0 images stored in the β0

database as described in Section II-A. Once the desired re-gion of interest and other input search parameters, such aspolarization, reference incidence angle (explained in the fol-lowing), and acquisition time interval, have been selected bythe user, the β0 database is accessed to retrieve a list of allthe available data for the required output map. Consideringthe backscatter dependence on the acquisition incidence angleand the vegetation class, the output backscatter map is referredto a precise output reference incidence angle αref . Note that,in the database, the acquisition incidence angle associated toeach input β0 pixel was evaluated as presented in Section II-A,without taking into account the local terrain slope. An interpola-tor is implemented to convert β0 values with different incidenceangles and surface classes to the reference output incidence

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3540 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011

Fig. 1. Interpolation to the output reference incidence angle. Algorithmflowchart.

angle αref . The corresponding algorithm flowchart is shown inFig. 1 and is explained in the following.

1) A ground vegetation class is associated to each inputβ0 pixel that suits the output map requirements. TheGLOBCOVER classification map [15], provided by ESAin a resolution of 300× 300 m2, is used for this pur-pose. A proper Ulaby model is then associated to eachGLOBCOVER class, as is presented in Table II. Note thatseveral GLOBCOVER classes, which are characterized bysimilar statistics, are mapped to the same class in theUlaby model.

2) The acquisition incidence angle of the considered β0

pixel is compared to the output reference incidence angleαref , and a correction factor is applied to the input β0

value. The X-band backscatter models retrieved from [10]are used to implement a series of correction curves. As anexample, Fig. 2 (top) shows the averaged pixel intensityversus the incidence angle for HH polarization and dif-ferent vegetation classes. For each considered vegetationclass, a correction curve has been computed by invertingsuch models [Fig. 2 (bottom)].

3) For an input pixel originally acquired with a local inci-dence angle α1 and characterized by a radar brightnessequal to β0

α1, the interpolation is performed as follows:

β0αref

= β0α1I(Δα) (1)

where β0αref

represents the pixel radar brightness referredto the output reference incidence angle αref , I is theincidence-angle-dependent correction curve associated tothe input pixel vegetation class, and Δα is evaluated as

Δα = α1 − αref . (2)

By applying the proper correction value to each input pixel,depending on its acquisition incidence angle and the vegetationclass, it is possible to generate a mosaic which is completely

TABLE II(LEFT) GLOBCOVER VEGETATION CLASSES [15] AND

(RIGHT) CORRESPONDENT ULABY MODEL [10]

referred to a single incidence angle αref . In this way, differentmaps can be generated for several output reference incidenceangles, starting from the same input data. Moreover, inputproducts acquired using a different polarization or frequencycan be used as well, as it will be presented in Section III. Notethat more than one input pixel value, e.g., from acquisitions atdifferent dates, can contribute to the same output cell of thebackscatter map to be generated. For this reason, different waysto combine multiple input pixels are considered as follows:

1) backscatter mean value, obtained by averaging all then available calibrated and interpolated β0

αref(k) val-

ues inside the single output resolution cell (where k =[1, . . . , n]);

2) backscatter maximum value, evaluated as the maximumvalue of all the available β0

αref(k) contributing to the same

output cell;3) backscatter standard deviation, evaluated as the sample

standard deviation of all the available β0αref

(k) valuesinside the considered output cell. A default value is setif only one input pixel is available inside the output cell.

Complementary information about the type of input dataused for the generation of the output backscatter map is pro-vided by the so-called type mask.

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Fig. 2. (Top) X-band backscatter dependence on the incidence angle and dif-ferent vegetation classes. Ulaby models (mean value) [10]. (Bottom) Correctioncurves referred to αref = 40◦.

Each map can be useful for different purposes. For example,the mean value can be used for performance estimation (SNR,height error, etc.), the maximum value for saturation levelevaluation (in order to avoid clipping), the standard deviationfor analyzing the image dynamic range and its impacts on datacompression rate [9], and the type mask for statistical analysison acquired SAR data. Fig. 3 shows an example of a generatedbackscatter map over Australia for HH polarization, a referenceincidence angle of 30◦, and an angular sampling of 0.05◦ inlatitude/longitude coordinates, which corresponds to a groundresolution of about 5× 5 km2 at the equator. Missing valuesover land areas in Fig. 3(A)–(C) are associated to the defaultvalue of −20 dB, which corresponds to the typical TSX-1 noisefloor. The color bar on the bottom refers to the type maskin Fig. 3(D). Each color classifies the input pixels used forgenerating the output map, particularly it can be inferred that:

1) Water bodies are masked off during the generation pro-cess, according to the information provided by GLOB-COVER, and associated to a default value (color bar classname: Water).

2) TSM data are classified depending on the acquired polar-ization and incidence angle. The incidence angles corre-spond to a series of ten reference angular intervals. Eachinterval is identified by its mean value, starting from 15◦

to 60◦, and is characterized by a spread within ±2.5◦.3) If TSM real data are missing over land areas, output cell

values are identified as No data.Note that, in Fig. 3(A), the averaging of the input pixels

results in a significant reduction of the backscatter dependenceon the acquisition polarization and incidence angle. However,few discontinuities are still present due to the limited numberof available input data (in most of the cases, only one single-pass image was available for averaging). With the availabilityof more repeat-pass data, this impact will be further reduced. In

Fig. 3. TSX-1 β0 backscatter map example over Australia for HH po-larization and 30◦ incidence angle. (A) Mean value. (B) Maximum value.(C) Standard deviation. (D) Type mask.

Fig. 3(B) and (C), these phenomena are more visible since noaveraging process is performed.

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3542 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011

Fig. 4. Stack of ten backscatter maps, which referred to ten different outputreference incidence angles αref,n (n = 1, . . . , 10), for HH polarization.

The quality of the final backscatter map depends on severalfactors, such as the following:

1) the radiometric calibration accuracy of TSX-1 products[14];

2) the geocoding accuracy [13];3) the accuracy and representativeness of the GLOBCOVER

classification map [15];4) the accuracy of the correction curves;5) the availability of X-band SAR data.

Given the high accuracy in both absolute calibration andgeocoding of TSX-1 products, the first two factors are notexpected to significantly influence the final global map quality.Due to surface changes, an incorrect vegetation class can beassociated with the considered ground region, leading to theapplication of an incorrect correction curve. Moreover, thecorrection curves are evaluated taking into account the Ulabymodel mean values only, without considering their dispersionfrom the expected value. However, since the standard deviationof the Ulaby models does not change considerably with theincidence angle [10], no significant error is expected to be in-troduced when the correction curves are applied to backscattervalues that substantially differ from the correspondent Ulabymodel mean value. Finally, the increasing availability of TSMdata leads to a better averaging of backscatter pixels inside thesame output cell.

C. Global Backscatter Map Final Structure

A stack of ten different output backscatter maps for each po-larization (HH, VV, and HV/VH) is generated, which referredto ten different output reference incidence angles αref,n (withn = 1, . . . , 10), as shown in Fig. 4. An incidence angle intervalfrom 15◦ to 60◦ was taken into account, and a backscatter mapevery 5◦ was generated. The input pixels contributing to eachoutput map were acquired with the same polarization as theoutput backscatter map and with an incidence angle within aspread of ±2.5◦ from the output backscatter map reference

Fig. 5. Filling of missing values. Algorithm flowchart.

incidence angle αref . The final global backscatter map has toallow the retrieval of a β0 value for any required polarizationand incidence angle within the TSM data collection range [12].For the required polarization, the user finally performs a linearinterpolation, in order to retrieve a backscatter value referred toa specific incidence angle, which differs from any of the alreadyavailable αref,n inside the stack.

III. FILLING OF MISSING VALUES

As noticed in Section II-B, the global Earth coverage withTSX-1 data is not available yet, neither with HH nor with VVpolarization. Since global backscatter map stacks are alreadyrequired in the commanding of the TSM and TDM, the gapsneed to be closed. For this reason, a dedicated algorithm forfilling the empty output cells has been developed (Fig. 5).Several steps are subsequently performed until the completecoverage is achieved. The first step consists of the selection ofthe so-called base map: Once the desired output polarizationand reference incidence angle αref have been chosen, thecorresponding backscatter map is selected from the stack ofbackscatter maps in Fig. 4. If the reference incidence angle ofthis base map differs from the desired output one, all pixels areinterpolated to the required output incidence angle using thecorrection procedure presented in Section II-B.

The second step consists in using TSX-1 data, acquired withdifferent incidence angles and polarizations, and interpolatingthem to the desired polarization and reference incidence angle,always using the correction curves presented in Section II-B. Inthe third step, small gaps are filled by averaging the nearestavailable samples inside a defined bidimensional window of

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Fig. 6. Global map. Backscatter mean value for HH polarization, referred to 30◦ incidence angle, generated using TSX-1 data, HH polarization only, anddifferent incidence angles.

Fig. 7. Global map. Backscatter mean value for HH polarization, referred to 30◦ incidence angle, after filling data gaps using the interpolation algorithmpresented in Section III.

20× 20 km2, assuming that backscatter characteristics remainalmost homogeneous among nearby pixels.

The fourth step consists in filling larger gaps by using dataretrieved from a C-band backscatter map provided by ESA[5], which is generated for HH polarization and referred to anincidence angle of 30◦. Based on the Ulaby models, the interpo-lation process described in Section II-B is first applied to the C-band mosaic, in order to convert it from C-band to X-band, andthen to the required output polarization and incidence angle.

The C-band mosaic does not provide a global coverage ofthe Earth: It is defined for latitudes included inside an intervalfrom −60◦ to 60◦, and not all land areas are mapped (e.g.,Madagascar and Indonesia are missing). For empty areas, theUlaby model and the GLOBCOVER classification map arefinally directly used to complete the output backscatter map.The standard deviation map (described in Section II-B) is set toa default value equal to zero if no real TSX-1 data are used forthe considered output cell.

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3544 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011

Fig. 8. Global map. Type mask associated to the backscatter map in Fig. 7. Each color corresponds to a different kind of data used for generating the final globalmap. The TSX-1 data are grouped together depending on the polarization. Different colors belonging to the same polarization class identify different incidenceangle intervals, whose details are omitted for the sake of clarity.

Fig. 9. Mosaic of mean backscatter β0 over Iceland, generated using sevendifferent ScanSAR products, acquired from December 2008 to February 2009(winter period), with HH polarization, in ascending orbit direction only. Groundresolution: 200× 200 m2. Reference incidence angle: 30◦. Water bodies arenot masked off.

IV. FIRST RESULTS OF GLOBAL AND ON-DEMAND

X-BAND BACKSCATTER MAPS

Fig. 6 shows the backscatter mean value, generated by usingall the available TSM data in HH polarization, acquired fromOctober 2008 to April 2010. It has a pixel spacing of 0.05◦ inlatitude/longitude coordinates. As can be seen, many regions,particularly over North America, Asia, and Antarctica, havenot been acquired yet. Fig. 7 shows the same map after theapplication of the algorithm for filling missing gaps presentedin Section III. Finally, Fig. 8 shows the type map associatedto the backscatter map in Fig. 7. Each color corresponds to adifferent kind of data used for the generation of the final mosaic,as explained in Section II-B. For the sake of clarity, the TSX-1data are grouped together depending on the polarization only,and no further details on the specific incidence angle interval

Fig. 10. Normalized histograms and Gaussian fittings of the HH backscattercorresponding to the vegetation class of Closed to open (> 15%) mixedbroadleaved and needleleaved forest (> 5 m) [15]. Each graph correspondsto a different incidence angle interval.

are presented. The values Window, Cband, and Ulaby identifythe three different approaches for filling missing values, aspresented in Section III.

The flexibility of the developed algorithm allows the gen-eration of output mosaics with particular characteristics. For

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example, the backscatter dependence on specific sensor pa-rameters can be analyzed, e.g., specific beam configurationsor satellite orbit directions. On the other hand, the choice ofa particular seasonal time for the input data provides a databasis for the monitoring of backscatter evolution in time orits dependence on soil conditions. Fig. 9 shows an example ofan on-demand backscatter map: a mosaic of mean backscatterover Iceland, generated by using seven different ScanSARproducts, acquired from December 2008 to February 2009during the winter period, with HH polarization, in ascendingorbit direction only. It is characterized by a ground resolutionof 200× 200 m2 and is referred to an incidence angle of 30◦.Water bodies are not masked off in order to better underline thesensor orbit direction.

V. STATISTICAL ANALYSIS OF BACKSCATTER BEHAVIOR

A statistical analysis of backscatter reflectivity measuredwithin the TSM is being performed in order to generate a setof up-to-date backscatter models for X-band. Since backscattermodels are a key component for the composition of the finalmosaic, as presented in Section II-B, a further objective of thispaper is to provide a validation approach for the backscattermap generation algorithm itself (Section II). The idea is tocharacterize TSM backscatter values, grouped together depend-ing on polarization channel, incidence angle, and vegetationclass, and to compare the measured backscatter dependenceon incidence angle against the one used inside the backscatterinterpolation algorithm. The GLOBCOVER classification mapis used to perform the classification. For each group, backscattervalues acquired within similar incidence angle intervals are sep-arately analyzed. The overall incidence angle spread of TSX-1is divided into N subintervals. The histogram of backscatterdata (in logarithmic scale) from an incidence angle subintervalis then evaluated. We suppose the radar backscatter to be log-normally distributed inside a single incidence angle interval sothat a Gaussian fitting of the normalized histogram can be per-formed. Fig. 10 shows the obtained results for radar backscatterdata, acquired in HH polarization, corresponding to the veg-etation class of Closed to open (> 15%) mixed broadleavedand needleleaved forest (> 5 m) [15]. Four different incidenceangle intervals are considered, and each plot displays the nor-malized histogram of radar backscatter, its mean value, the 5%and 95% percentiles, the correspondent Gaussian fitting, andits mean value μ. The mean values μi (i = 0, . . . , N) can nowbe used to generate an incidence-angle-dependent backscattermodel for the considered vegetation class, by performing anonlinear fitting. The following function has been used tomodel the backscatter behavior:

S(c, α) = c0 + c1 exp(−c2α) + c3 cos(c4α+ c5) (3)

where c = [c0, . . . , c5] represents a vector of fitting coefficientsand α is the incidence angle [10]. The sum of a cosine and anexponential function is well suited to modeling a trend with thepossibility of small oscillations.

The function presented in (3) may possess local minima. Forthis reason, a technique that is able to find the global minimumeven in such cases has to be used to perform a suitable fitting.

Fig. 11. (Top) Backscatter dependence on incidence angle, derived fromthe histograms in Fig. 10, by applying the simulated annealing optimizationalgorithm. (Bottom) Weights associated to each input measure during theoptimization process.

One solution is to perform the optimization of a cost functionusing the simulated annealing algorithm [16]. The cost functionto be optimized is the fitting model S(c, α) in (3), while theparameter that characterizes each state of the system E canbe modeled as the weighted mean-square error between theretrieved Gaussian mean values and the correspondent fittingfunction

E =

N∑

i=1

wi (μi − S(c, αi))2 (4)

where w = [w1, . . . , wN ] is a vector of weights for each singlemeasure which takes into account several factors, such as themeasure reliability, the number of samples used to generate thehistogram, and the mean-square error between the histogramand the fitted Gaussian probability density function. Thealgorithm randomly permutes the vector of fitting coefficientsc, following a slowly decreasing function schedule, untilconvergence is achieved. Fig. 11 shows the optimization results(upper plot) and the correspondent weights associated to eachinput measure (lower plot). The incidence angle associated toeach fitted measure is evaluated as the mean value of all theincidence angles, associated to the input backscatter valuesinside the considered angular interval.

Up to now, this analysis has been performed for the vege-tation class shown in Fig. 10 [Closed to open (> 15%) mixedbroadleaved and needleleaved forest (> 5 m)]. A future workto be performed is the extension of the statistical analysis to allthe available ground vegetation classes, incidence angles, andpolarizations, given the up-to-date database of acquired TSX-1 images. This leads to the generation of a complete archive ofreliable models for X-band backscatter, based on the TSM SARimage database. The validation of the backscatter interpolationalgorithm can be performed by comparing the correction curvesin Fig. 2 (bottom) with the ones that are derived from the actualTSM measures. Fig. 12 (top) shows the backscatter correctioncurves derived from the TSX-1 data for the vegetation class ofClosed to open (> 15%) mixed broadleaved and needleleavedforest (> 5 m) (red) and from the Ulaby model for Trees (blue).The reference incidence angle is αref = 40◦. A maximum

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3546 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 10, OCTOBER 2011

Fig. 12. (Top) Backscatter correction curves derived (red) from the TSX-1data for the vegetation class of Closed to open (> 15%) mixed broadleavedand needleleaved forest (> 5 m) and (blue) from the Ulaby model for Trees.Reference incidence angle αref = 40◦. (Bottom) Difference between the twocorrection curves.

difference of about 0.4 dB has been detected between the twocorrection curves, as shown in Fig. 12 (bottom).

VI. CONCLUSION

In this paper, the generation of X-band backscatter mapshas been discussed on the example of using TSX-1 data. Theapproach and the first results obtained by generating global andon-demand backscatter maps have been presented. The outputset consists in a series of different maps, with each of themvaluable for different kinds of applications inside the TSMand TDM, as well as for scientific investigations. Until thefull Earth coverage is reached by the TDM, gaps need to befilled. An algorithm has been proposed in order to fill gapswith corrected data acquired from other incidence angles, po-larizations, and neighbor samples or even data acquired with adifferent wavelength. The already generated global backscattermap allows for the enhancement of current applications, such asaccurate instrument commanding and performance estimationinside the TSM and TDM, as well as for new applicationslike the monitoring of backscatter with time in X-band. Thegeneration of on-demand backscatter maps allows for the mon-itoring of X-band backscatter variability with sensor parametersor acquisition and seasonal times. A validation approach of themap generation process in Section II has been presented, anda statistical analysis example of X-band backscatter behaviorhas been shown. A future work will be the generation of acomplete set of statistical models, based on spaceborne SARimages, which characterize the X-band backscatter for differentvegetation classes, polarizations, and incidence angles.

ACKNOWLEDGMENT

The authors would like to thank P. Snoeij at the EuropeanSpace Research and Technology Centre of the European SpaceAgency and the Technische Universität Wien team for provid-ing the C-band backscatter mosaic. The authors would also liketo thank J. F. Cores from the Instituto Nacional de Técnica

Aeroespacial, Spain, who worked on the topic TerraSAR-Xbackscatter map generation during his stay as guest scientistat the German Aerospace Center (DLR) in summer 2008, forthe valuable inputs.

REFERENCES

[1] G. Krieger, A. Moreira, H. Fiedler, I. Hajnsek, M. Werner,M. Younis, and M. Zink, “TanDEM-X: A satellite formation forhigh-resolution SAR interferometry,” IEEE Trans. Geosci. Remote Sens.,vol. 45, no. 11, pp. 3317–3341, Nov. 2007.

[2] M. Werner, “Shuttle Radar Topography Mission (SRTM): Experiencewith the X-band SAR interferometer,” in Proc. CIE Int. Conf. Radar,Beijing, China, Oct. 2001, pp. 634–638.

[3] F. Covello, F. Battazza, A. Coletta, M. L. Battagliere, V. Bellifemine, andL. Candela, “One-day interferometry results with the COSMO-SkyMedconstellation,” in Proc. IGARSS, Honolulu, HI, Jul. 2010, pp. 4397–4400.

[4] S. Lee, “Overview of KOMPSAT-5 program, mission, and system,” inProc. IGARSS, Honolulu, HI, Jul. 2010, pp. 797–800.

[5] D. Sabel, M. Doubkova, W. Wagner, and Z. Bartalis, “Sigma nought statis-tics over land activity—Final report,” Inst. Photogramm. Remote Sens.,Vienna Univ. Technol., Vienna, Austria, ESA contract 22122/08/NL/JA,2009.

[6] M. Shimada and T. Ohtaki, “Generating large-scale high-quality SARmosaic datasets: Application to PALSAR data for global monitoring,”IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 3, no. 4,pp. 637–656, Dec. 2010.

[7] S. Buckreuss, R. Werninghaus, and W. Pitz, “German satellite missionTerraSAR-X,” in Proc. IEEE Conf., Rome, Italy, 2008, pp. 1–5.

[8] B. Bräutigam, P. Rizzoli, C. Gonzales, M. Weigt, D. Schrank, D. Schulze,and M. Schwerdt, “SAR performance monitoring for TerraSAR-X mis-sion,” in Proc. IGARSS, Honolulu, HI, 2010, pp. 3454–3457.

[9] J. Mittermayer, M. Younis, R. Metzig, S. Wollstadt, J. Márquez, andA. Meta, “TerraSAR-X system performance characterization and verifi-cation,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 660–676,Feb. 2010.

[10] F. T. Ulaby and M. C. Dobson, Handbook of Radar Scattering Statisticsfor Terrain. Norwood, MA: Artech House, 1989.

[11] D. Small, N. Miranda, and E. Meier, “A revised radiometric normalizationstandard for SAR,” in Proc. IGARSS, Cape Town, South Africa, 2009,pp. IV-566–IV-569.

[12] T. Fritz, M. Eineder, H. Breit, B. Schättler, E. Boerner, and M. Huber,“The TerraSAR-X basic products—Format and expected performance,”presented at the Eur. Conf. Synthetic Aperture Radar, Dresden, Germany,2006.

[13] T. Fritz and M. Eineder, TerraSAR-X Ground Segment Basic ProductSpecification Document, no. 1.6, Mar. 18, 2009.

[14] M. Schwerdt, B. Bräutigam, M. Bachmann, B. Döring, D. Schrank, andH. Hueso Gonzalez, “Final TerraSAR-X calibration results based on novelefficient methods,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2,pp. 677–689, Feb. 2010.

[15] GLOBCOVER Products Description Manual. [Online]. Available: http://ionia1.esrin.esa.int/docs/GLOBCOVER_Product_Specification_v2.pdf

[16] S. Kirkpatrick, C. Gelatt, and M. Vecchi, “Optimization by simulatedannealing,” Science, vol. 220, no. 4598, pp. 671–680, May 1983.

Paola Rizzoli received the M.Sc. degree in telecom-munication engineering from the Politecnico di Mi-lano, Milano, Italy, in 2006, with a thesis on theestimation of azimuth antenna pattern from syntheticaperture radar (SAR) images.

From 2006 to 2008, she worked as Research andProject Engineer at the Politecnico di Milano andAresys s.r.l., a Politecnico di Milano spin-off com-pany, being mainly involved in cooperation projectswith the European Space Agency. In 2008, she joinedthe Satellite SAR Systems Department, Microwaves

and Radar Institute, German Aerospace Center (DLR), Wessling, Germany. Sheis currently working on SAR system design and performance estimation for theTerraSAR-X and TanDEM-X missions.

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RIZZOLI et al.: RADAR BACKSCATTER MAPPING USING TerraSAR-X 3547

Benjamin Bräutigam received the Dipl.Ing. degreein electrical engineering from the Universität Karl-sruhe (TH), Karlsruhe, Germany, in 2003.

In 2003, he was a Visiting Scientist at the Envi-ronmental Technology Laboratory, National Oceanicand Atmospheric Administration, Boulder, CO. In2004, he joined the Microwaves and Radar Insti-tute, German Aerospace Center (DLR), Wessling,Germany. He was responsible for internal instru-ment calibration in spaceborne synthetic apertureradar projects like TerraSAR-X, TanDEM-X, and

Sentinel-1. Since 2008, he has been the Head of the System PerformanceGroup, Microwaves and Radar Institute, DLR, working on TerraSAR-X andTanDEM-X. His major research interests include the development and analysisof innovative methods for instrument calibration and performance monitoring.

Steffen Wollstadt received the Dipl.-Ing. (M.Sc.)degree in electrical engineering from the TechnicalUniversity of Darmstadt, Darmstadt, Germany, in2005, with a thesis on metamaterial-based microstripantenna design.

In 2006, he joined the Microwaves and Radar In-stitute, German Aerospace Center (DLR), Wessling,Germany. From 2006 to 2008, he worked in theSatellite SAR Systems Department, Microwaves andRadar Institute, DLR, as Project Engineer in theSystem Engineering and Calibration part of the

TerraSAR-X Ground Segment. He worked on the TerraSAR-X and TanDEM-X missions. Since 2009, he has been with the Radar Concepts Department,Microwaves and Radar Institute, DLR, where he is currently working on TerrainObservation by Progressive Scans. Sentinel-1 image quality and syntheticaperture radar performance investigations.

Josef Mittermayer was born in Wartenberg, Ober-bayern, Germany, in 1967. He received the Dipl.Ing.degree in electrical engineering, with a thesis onScanSAR processing, from the Technical Universityof Munich, Munich, Germany, in 1995, the Ph.D. de-gree in the field of Spotlight synthetic aperture radar(SAR) processing from the University of Siegen,Siegen, Germany, in 2000, and the M.Sc. degree inspace system engineering from Delft University ofTechnology, Delft, The Netherlands, in 2004.

From 1994 to 2001, he was with the Signal Pro-cessing Group, Microwaves and Radar Institute, German Aerospace Center(DLR), Wessling, Germany. Since 2002, he has been working in the TerraSAR-X Project at DLR. From January 2004 until the end of the commissioningphase in 2008, he was the Group Leader and Project Manager of SystemEngineering and Calibration, which is one of the three subprojects which formthe TerraSAR-X Ground Segment. In addition, he was technically responsiblefor the TerraSAR-X commissioning phase. He is currently a Scientist with theMicrowaves and Radar Institute, DLR, in the fields of SAR system engineeringand SAR processing.

Dr. Mittermayer and his colleagues were the recipient of the IEEE Geo-science and Remote Sensing Society Transactions Prize Paper Award for apaper on air- and spaceborne stripmap and ScanSAR processing in 1996. Hewas also the recipient of the DLR Science Award for his work on SpotlightSAR processing in 2001.